The Learning Conditions Monitor 2010
Bibliographic record
Abstract
Learning Conditions Monitor has been conducted periodically by Statistics Norway as an additional survey to the Labour Force Survey (AKU) every 1st quarter since 2003. The survey aims to identify the conditions for learning and skills among adults, with particular emphasis on working life. The survey maps the actual participation in formal and non-formal education, courses, seminars, conferences and the like. Moreover are mapped the respondents' needs and desires to participate in these forms of learning. Furthermore, it is asked how the current education and training is organized, as well as conditions for participation, for example salary during training, barriers to participation in various forms of learning, employer attitudes, etc. These are all important indicators of the development of the knowledge and learning community. The survey contains questions from Eurostat additional survey on lifelong learning that is conducted every few years in all EU countries and candidate countries, as well as some additional questions that only are asked in Norway. FAFO is responsible for the design of the survey and analysis, while Ministry of Education and Research is responsible for funding. Statistics Norway are primarily responsible for the data collection. For more information on AKU: http://www.nsd.uib.no/nsddata/serier/akuundersokelsene.html
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.030 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".